Image processing apparatus and method

ABSTRACT

An image processing apparatus includes a calculator configured to calculate a respective position offset for each of a plurality of candidate areas in a second frame based on a position of a basis image in a first frame and a determiner configured to determine a final selected area that includes a target in the second frame based on a respective weight allocated to each of the plurality of candidate areas and the calculated respective position offset.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority from Chinese Patent Application No.201611025103.9, filed on Nov. 15, 2016 in the State IntellectualProperty Office of the People's Republic of China, and Korean PatentApplication No. 10-2017-0058547, filed on May 11, 2017 in the KoreanIntellectual Property Office, the disclosures of which are incorporatedherein by reference in their entireties.

BACKGROUND 1. Field

Methods and apparatuses consistent with example embodiments relate toimage processing technology.

2. Description of the Related Art

Portable terminals such as a smartphone, a tablet personal computer (PC)and a lap top computer are being used widely. Further, it has becomepossible to continuously capture a target using a capturing device inthe portable terminal. In order to acquire a clear frame in acontinuously captured video file, a process of tracking a target andadjusting a focus for the target may be required.

A target tracking method may include, for example, a method ofperforming a selective search on a predetermined bounding box of aplurality of areas and a method of tracking a target in a frame using aparticle filter that predicts a stochastic movement of each point.

SUMMARY

Example embodiments may address at least the above problems and/ordisadvantages and other disadvantages not described above. Further, theexample embodiments are not required to overcome the disadvantagesdescribed above, and an example embodiment may not overcome any of theproblems described above.

According to an aspect of an example embodiment, there is provided animage processing apparatus including a processor configured to implementa calculator configured to calculate a respective first position offsetfor each of a plurality of candidate areas in a second frame based on aposition of a basis image in a first frame and a determiner configuredto determine a final selected area that includes a target in the secondframe based on a respective weight allocated to each of the plurality ofcandidate areas and the calculated respective first position offset.

The determiner may be further configured to determine each respectiveweight based on a respective position of each corresponding one of theplurality of candidate areas in the second frame.

The calculator may be further configured to calculate a plurality ofsecond position offsets by applying a feature regression matrix to eachof the plurality of candidate areas and to calculate a target positionoffset for tracking the target by applying each respective weight toeach corresponding one of the plurality of second position offsets.

The calculator may be further configured to calculate a plurality ofsecond position offsets for a first candidate area by using a pluralityof predetermined feature regression matrices, and to calculate the firstposition offset that corresponds to the first candidate area by using anaverage value of the plurality of second position offsets. The pluralityof predetermined feature regression matrices may be determined based ona respective third position offset that corresponds to a respectivefeature point of each of a plurality of sample frames and a featurepoint in the basis image.

The image processing apparatus may further include an extractorconfigured to determine an initial selected area associated with thetarget in the second frame based on the basis image in the first frameand to extract the plurality of candidate areas based on the determinedinitial selected area. The extractor may be further configured tocalculate an overall position offset between the first frame and thesecond frame and to determine the initial selected area based on thecalculated overall position offset and information that relates aposition at which the target is present in the basis image.

The extractor may be further configured to extract, from the secondframe, respective projection points that correspond to feature points inthe basis image and to determine the overall position offset by using arespective texture value of each of a plurality of first points in apredetermined range and the extracted projection points. The extractormay be further configured to extract the plurality of first points inthe predetermined range based on the extracted projection points, todetermine matching points that correspond to the feature points based ona respective similarity between a texture value of each correspondingone of the extracted plurality of first points and a texture value ofeach of the feature points, and to determine the overall position offsetby comparing a respective position of each of the feature points with arespective position of each of the matching points.

The image processing apparatus may further include a storage configuredto store the second frame in which the final selected area is determinedand to update, when a number of stored frames is greater than or equalto a threshold, the basis image based on a target tracking result valueof the stored frames.

According to another aspect of an example embodiment, there is alsoprovided an image processing method including calculating a respectivesimilarity between a positive sample associated with a target and eachof a plurality of candidate areas and determining a final selected areathat includes the target in a frame based on each calculated similarity.

The calculating of the respective similarity may include comparing afeature of the positive sample included in a sparse subspace clustering(SSC) model to a feature of a respective subarea in each of theplurality of candidate areas.

The calculating of the respective similarity may include calculating asimilarity that corresponds to a first candidate area based on a sum ofsimilarities between a plurality of subareas included in the firstcandidate area.

The SSC model may be determined by using a plurality of sample framesbased on a Euclidean distance between the positive sample associatedwith the target and a negative sample associated with a feature of anarea that is adjacent to the final selected area that includes thetarget.

The image processing method may further include comparing a similaritybetween the positive sample and the final selected area of the frame toan average value of similarities between the positive sample andprevious frames and storing the frame based on a comparison result. Theimage processing method may further include comparing a number of storedframes to a threshold and updating an SSC model by using the storedframes as sample frames based on the comparison result.

According to still another aspect of an example embodiment, there isalso provided an image processing apparatus including an extractorconfigured to extract a plurality of candidate areas from an input frameby using a basis image, a first calculator configured to calculate atarget position offset for tracking respective feature points includedin each of the plurality of candidate areas, a second calculatorconfigured to calculate a respective similarity between each of theplurality of candidate areas and a positive sample associated with atarget, and a determiner configured to determine a final selected areathat includes the target by applying a first weight to the targetposition offset and applying a respective second weight to eachrespective similarity between the corresponding one of the plurality ofcandidate areas and the positive sample.

The first calculator may be further configured to calculate a pluralityof first position offsets by applying a feature regression matrix toeach corresponding one of the plurality of candidate areas, and tocalculate the target position offset by applying a weight to theplurality of first position offsets.

The second calculator may be further configured to calculate eachrespective similarity based on a hybrid sparse subspace clustering(HSSC) model that is determined by using the positive sample associatedwith the target and a negative sample associated with an area that isadjacent to the final selected area that includes the target.

According to yet another aspect of an example embodiment, there is alsoprovided a target tracking method including an acquiring a candidatearea associated with a target in a current frame and acquiring a finalselected area by performing a feature regression on the acquiredcandidate area.

The acquiring of the candidate area may include determining informationthat relates to an initial selected area of the target of the currentframe based on a basis image of the target of a prestored frame andacquiring information that relates to a first set number of candidateareas around the initial selected area of the target.

The acquiring of the final selected area may include performingregression with respect to information that relates to each candidatearea acquired based on a feature regression matrix and determining thefinal selected area of the target based on information obtained after atotal regression of the candidate area acquired by performing theregression.

The acquiring of the final selected area may further include performingthe feature regression and a feature assessment on the acquiredcandidate area.

According to a further aspect of an example embodiment, there is alsoprovided a target tracking method including acquiring a candidate areaassociated with a target of a current frame and acquiring a finalselected area by performing a feature assessment on the acquiredcandidate area, wherein the feature assessment is performed based on anSSC model.

The acquiring of the final selected area may include performing anassessment on information that relates to each candidate area acquiredusing the SSC model and determining the final selected area of thetarget based on information that relates to a candidate area thatcorresponds to a maximal assessment value acquired as a result of theassessment.

The performing of the assessment may include performing an assessment onan image feature of a respective sub-candidate area of each candidatearea acquired using the SSC model, determining an assessment value ofthe image feature of the candidate area based on an assessment value ofthe image feature of the corresponding sub-candidate area, anddetermining a maximal assessment value from the assessment value of theimage feature of the candidate area.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and/or other aspects will be more apparent by describingcertain example embodiments with reference to the accompanying drawings,in which:

FIG. 1 is a diagram illustrating an operation of an image processingapparatus, according to an example embodiment;

FIG. 2 is a block diagram illustrating an image processing apparatus,according to an example embodiment;

FIGS. 3A and 3B are diagrams illustrating a process of determining aninitial selected area based on a basis image by using the imageprocessing apparatus of FIG. 2;

FIG. 4 is a flowchart illustrating a process of determining a finalselected area that includes a target by using the image processingapparatus of FIG. 2;

FIG. 5 is a flowchart illustrating a process of training an imageprocessing apparatus on a feature regression matrix, according to anexample embodiment;

FIG. 6 is a flowchart illustrating a process of determining a finalselected area that includes a target by using an image processingapparatus, according to an example embodiment;

FIGS. 7A and 7B are diagrams illustrating a process of extracting apositive sample and a negative sample based on a sample frame by usingan image processing apparatus, according to an example embodiment;

FIG. 8 is a flowchart illustrating a process of training the imageprocessing apparatus of FIG. 7A on a sparse subspace clustering (SSC)model; and

FIG. 9 is a block diagram illustrating an image processing apparatus,according to an example embodiment.

DETAILED DESCRIPTION

Example embodiments are described in greater detail herein withreference to the accompanying drawings.

In the following description, like drawing reference numerals are usedfor like elements, even in different drawings. The matters defined inthe description, such as detailed construction and elements, areprovided to assist in a comprehensive understanding of the exampleembodiments. However, it will be apparent to persons having ordinaryskill in the art that the example embodiments can be practiced withoutthose specifically defined matters. Also, well-known functions orconstructions are not described in detail since they would obscure thedescription with unnecessary detail.

In addition, the terms such as “unit”, “-er (-or)”, and “module”described in the specification refer to an element for performing atleast one function or operation, and may be implemented in hardware,software, or the combination of hardware and software.

In the following description, like drawing reference numerals are usedfor like elements, even in different drawings. Various alterations andmodifications may be made to the example embodiments, some of which willbe illustrated in detail in the drawings and detailed description. Thematters defined in the description, such as detailed construction andelements, are provided to assist in a comprehensive understanding of theexample embodiments. However, it should be understood that these exampleembodiments are not construed as limited to the illustrated forms andinclude all changes, equivalents or alternatives within the idea and thetechnical scope of this disclosure. It will be apparent to personshaving ordinary skill in the art that the example embodiments can bepracticed without those specifically defined matters. Also, well-knownfunctions or constructions are not described in detail since they wouldobscure the description with unnecessary detail.

The features described herein may be embodied in different forms, andare not to be construed as being limited to the examples describedherein. Rather, the examples described herein have been provided so thatthis disclosure will be thorough and complete, and will convey the fullscope of the disclosure to one of ordinary skill in the art.

Terms such as first, second, A, B, (a), (b), and the like may be usedherein to describe components. Each of these terminologies is not usedto define an essence, order or sequence of a corresponding component butused merely to distinguish the corresponding component from othercomponent(s). For example, a first component may be referred to a secondcomponent, and similarly the second component may also be referred to asthe first component.

The terminology used herein is for the purpose of describing particularexamples only, and is not to be used to limit the disclosure. As usedherein, the terms “a,” “an,” and “the” are intended to include theplural forms as well, unless the context clearly indicates otherwise. Asused herein, the terms “include, “comprise,” and “have” specify thepresence of stated features, numbers, operations, elements, components,and/or combinations thereof, but do not preclude the presence oraddition of one or more other features, numbers, operations, elements,components, and/or combinations thereof.

Unless otherwise defined, all terms, including technical and scientificterms, used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this disclosure pertains. Terms,such as those defined in commonly used dictionaries, are to beinterpreted as having a meaning that is consistent with their meaning inthe context of the relevant art, and are not to be interpreted in anidealized or overly formal sense unless expressly so defined herein.

FIG. 1 is a diagram illustrating an operation of an image processingapparatus, according to an example embodiment. An image processingapparatus 100 may be implemented as a computing device. The imageprocessing apparatus 100 may be implemented as, for example, at leastone software module, at least one hardware module such as amicroprocessor or integrated circuit, or combinations thereof such as aprocessor executing a software program or instructions. Referring toFIG. 1, the image processing apparatus 100 may receive an input imageand determine a final selected area that includes a target in thereceived input image. Hereinafter, the target may indicate an objectthat is tracked in image data as a region of interest of a user. Thetarget may be designated as, for example, an eye and/or a nose of apredetermined user in a frame of an overall image. Also, the target maybe designated as a position of a predetermined player in a sport imagein which a plurality of players is moving.

To determine the final selected area that includes the target, the imageprocessing apparatus 100 may use a basis image that has previously beenstored. Hereinafter, the basis image may indicate an area verified tohave a target in a predetermined frame. The basis image may be stored inany of various forms, for example, a polygonal area, an oval area, andan irregular-shaped area connecting a plurality of pixel points. Thepolygonal area may include, for example, a triangular area, a pentagonalarea, a five-pointed star area, and a hexagonal area.

The image processing apparatus 100 may determine a final selected area110 in the first frame F1 based on a position of the basis image.Similarly, the image processing apparatus 100 may determine a finalselected area 120 in an nth frame Fn. The image processing apparatus 100may update the basis image based on a number of frames in which a finalselected area is determined. A process of selecting a basis image andupdating the selected basis image will be also described in detailbelow.

FIG. 2 is a block diagram illustrating an image processing apparatus,according to an example embodiment. Referring to FIG. 2, an imageprocessing apparatus 200 may include an extractor 210, a calculator 220,a determiner 230, and a storage 240. The extractor 210, the calculator220, and the determiner 230 may be implemented by a processor. Theextractor 210 may determine an initial selected area associated with atarget in a second frame based on a basis image in a first frame. Forexample, the first frame may be a predetermined frame included in anoverall input image. In addition, the first frame may be a frame inwhich information that relates to the basis image is stored in advance.The basis image may be an image of an area that includes the target inthe first frame. For example, the information that relates to the basisimage may be information associated with feature points included in thebasis image. Further, the second frame may be a frame to be input to theimage processing apparatus 200 after the first frame in the overallinput image.

The extractor 210 may extract a plurality of candidate areas based onthe initial selected area determined in the second frame. For example,the extractor 210 may extract a predetermined number of candidate areasbased on the initial selected area associated with the target.

The calculator 220 may calculate a respective position offset for eachof the plurality of candidate areas in the second frame based on aposition of the basis image in the first frame. The calculator 220 maycalculate each respective position offset by performing a featureregression on each corresponding one of the plurality of candidateareas. Hereinafter, the feature regression may indicate a process oftracking an offset of a position to which feature points in the basisimage are relocated with respect to each of the candidate areas.

The calculator 220 may calculate a plurality of position offsets byapplying a pre-trained feature regression matrix to each of theplurality of candidate areas. Hereinafter, the feature regression matrixmay indicate a matrix that defines respective differences betweenpositions of feature points in a basis image and corresponding positionsof feature points in a candidate area. The image processing apparatus200 may compare a position of the basis image that includes the targetto a corresponding position of each of the candidate areas based on thefeature regression matrix. Based on a comparison result, the imageprocessing apparatus 200 may track a candidate area that has a mostsimilar feature to that of the basis image.

The calculator 220 may calculate a target position offset for trackingthe target by applying a respective weight to each of the plurality ofposition offsets.

The determiner 230 may determine a final selected area that includes thetarget in the second frame based on the respective weight allocated toeach corresponding one of the plurality of candidate areas and thecalculated position offset. The determiner 230 may determine eachrespective weight based on a position at which each of the plurality ofcandidate areas is present in the second frame.

The storage 240 may store the second frame that has the determined finalselected area in a memory included in the image processing apparatus200. When a number of stored frames is greater than or equal to athreshold, the storage 240 may update the basis image based on a targettracking result value of the stored frames.

Further, the storage 240 may update the feature regression matrix byusing a newly stored frame. The storage 240 may replace a featureregression matrix that has not been updated for the longest period oftime with a new feature regression matrix.

FIGS. 3A and 3B are diagrams illustrating a process of determining aninitial selected area based on a basis image by using the imageprocessing apparatus 200 of FIG. 2. Referring to FIG. 3A, the imageprocessing apparatus 200 may determine an initial selected area 340 in asecond frame F2 which has been newly input by using a first frame F1which had previously been stored. The extractor 210 may determine aplurality of matching points 330 in the second frame F2 based on featurepoints 320 in a basis image 310. Hereinafter, a feature point mayindicate a point to be identified by an image processing apparatus amongpixel points in a frame. The feature point may be defined as points,such as, for example, a face contour point and a face element point suchas an eye, a nose, a mouth, and the like of a target.

The extractor 210 may extract a plurality of projection points in thesecond frame F2 of the input image by using the feature points 320 ofthe prestored first frame F1. Hereinafter, a projection point mayindicate a pixel point in a second frame that corresponds to at leastone of the feature points 320. The extractor 210 may extract pixelpoints included in a predetermined range based on the projection point.For example, the extractor 210 may extract points that correspond to a3×3 matrix as the pixel points based on a position of the projectionpoint. The 3×3 matrix is merely an example, and example embodiments arenot limited to this example. In addition, pixel points in various rangesmay be extracted in accordance with a selection of a person havingordinary skill in the art.

The extractor 210 may compare a respective texture value of each of thefeature points 320 to a respective texture value of each correspondingone of the extracted pixel points and determine matching points in thesecond frame based on a comparison result. For example, the extractor210 may determine pixel points that have the highest similarity withrespect to the texture value of the feature points 320 to be thematching points of the second frame.

The extractor 210 may calculate a texture gradient value of theextracted pixel points and the projection point and extract pixel pointsthat correspond to the calculated texture gradient being greater than apredetermined threshold as a candidate matching point. Further, theextractor 210 may compare the texture value of the feature points 320 tothe texture value of the candidate matching points and determine thematching points 330 in the second frame based on a comparison result.The extractor 210 may determine pixel points that have the highestsimilarity with respect to the texture value of the feature points 320to be the matching points 330 in the second frame. As such, the imageprocessing apparatus may select an initial selected area for tracking atarget based on a protection point, thereby increasing accuracy andefficiency of the target tracking.

The extractor 210 may compare respective positions of the matchingpoints 330 in the second frame F₂ to the positions of the feature points320 in the first frame F₁ and determine an overall position offsetassociated with the second frame F₂ based on a comparison result.

The extractor 210 may determine the overall position offset associatedwith the second frame F₂ of the input image by using a respective weightthat corresponds to each of the feature points 320. The extractor 210may calculate position offsets between the feature points 320 and thematching points 330 and calculate a weighting average value obtained byapplying each respective weight to the calculated position offsets asthe overall position offset associated with the second frame F₂. Therespective weight may be determined based on a similarity between therespective texture value of each of the feature points 320 and therespective texture value of each corresponding one of the matchingpoints 330.

The extractor 210 may calculate area information of the initial selectedarea 340 in the second frame F₂ based on area information of the basisimage 310 in the first frame F₁ and the calculated overall positionoffset. The area information may include at least one of, for example,image data in an area, size information of the area, positioninformation of the area in a frame, and feature points in the area.

The extractor 210 may estimate target position information in the secondframe F₂ based on the position information of the basis image 310 in thefirst frame F₁ and the overall position offset associated with thesecond frame F₂. The target position information may indicate a positionat which the basis image 310 is present in the second frame F₂. Further,the extractor 210 may determine the initial selected area 340 of thetarget in the second frame F₂ based on the estimated target positioninformation. For example, the initial selected area 340 and the basisimage 310 may be the same in size.

Referring to FIG. 3B, the image processing apparatus 200 may extract aplurality of candidate areas 351, 352, 353, and 354 based on theextracted initial selected area 340. The extractor 210 may sample acandidate area X_(i) (i=1, . . . , N, N being a positive integer) in anarea that is adjacent to the initial selected area 340 and acquire areainformation associated with the candidate area X_(i). The areainformation may include, for example, information that relates tofeature points 331, 332, 333, 334, 335, and 336 included in the area,position information, and size information of the candidate area X_(i).

When the basis image includes at least two basis sub-images, theextractor 210 may acquire information that relates to a respectivesub-candidate area that corresponds to each of the basis sub-imagesbased on the extracted candidate area. For example, a single basis imagethat includes four basis sub-images may be stored in a memory. In thisexample, the extractor 210 may extract a respective sub-candidate areathat corresponds to each of the four basis sub-images and acquireinformation that corresponds to each sub-candidate area.

FIG. 4 is a flowchart illustrating a process of determining a finalselected area that includes a target by using the image processingapparatus of FIG. 2. Referring to FIG. 4, an image processing apparatusmay calculate a plurality of position offsets by applying a featureregression matrix to each of a plurality of candidate areas in operation410, may calculate a target position offset by applying a respectiveweight to each of the plurality of position offsets in operation 420,and may determine a final selected area that includes a target by usingthe target position offset in operation 430.

In operation 410, the image processing apparatus may perform a featureregression to calculate a respective difference value between a positionof a basis image in a first frame and a representative position of eachcandidate area in a second frame. The image processing apparatus maycalculate a plurality of position offsets by applying a pre-trainedfeature regression matrix to each of the candidate areas.

For example, a representative position of a candidate area may be (x,y). In this example, the feature regression matrix H may be defined as amatrix “H=[h₁,h₂]” used to perform a feature regression on therepresentative position (x, y) to be in the basis image. The featureregression matrix may be a matrix that is determined based ondifferences in position between each respective one of the featurepoints in the basis image and each corresponding one of the featurepoints in the sample frame. A process of generating the featureregression matrix via machine learning will be described in detail withreference to the drawings below.

The image processing apparatus may calculate a position offset Ti thatcorresponds to an candidate area X_(i) (i=1, . . . , N, N being apositive integer) based on a feature point q_(i) included in thecandidate area X_(i) by applying Equation 1 below.

T _(i) =H ^(T) ×q _(i)  [Equation 1]

In Equation 1, H^(T) denotes a transposed matrix of the featureregression matrix. According to Equation 1, the image processingapparatus may calculate the respective position offset T_(i) (i=1, . . ., N, N being a positive integer) that corresponds to each of N sampledcandidate areas.

A plurality of feature regression matrices H_(j) (j=1, . . . , M, Mbeing a positive integer) may be previously determined based on aplurality of sample frames. In this example, the image processingapparatus may calculate a plurality of position offsets by repetitivelyapplying the plurality of the feature regression matrices to each of thecandidate areas. The image processing apparatus may calculate therespective position offset T that corresponds to the candidate areaX_(i) by applying Equation 2 below.

T _(i) ^(j) =H _(j) ^(T) ×q _(i)  [Equation 2]

In Equation 2, q_(i) denotes a feature point included in the candidatearea X_(i) (i=1, . . . , N, N being a positive integer), and T_(i) ^(j)denotes a position offset calculated by applying a j^(th) featureregression matrix to an i^(th) candidate area X_(i). In addition, inEquation 2, H_(j) ^(T) denotes a transposed matrix of the j^(th) featureregression matrix. The image processing apparatus may calculate thei^(th) position offset Ti that corresponds to the i^(th) candidate areaX_(i) by applying Equation 3 below.

$\begin{matrix}{T_{i} = {\frac{1}{M}{\sum\limits_{j}T_{i}^{j}}}} & \left\lbrack {{Equation}\mspace{14mu} 3} \right\rbrack\end{matrix}$

The image processing apparatus may calculate an average value ofposition offsets calculated by using the plurality of feature regressionmatrices to calculate the i^(th) position offset Ti that corresponds tothe i^(th) candidate area X_(i).

In operation 420, the image processing apparatus may calculate a targetposition offset for tracking the target by applying a respective weightto each of the plurality of position offsets T_(i). The image processingapparatus may determine each respective weight based on a variance V_(i)of the position offsets T_(i) ^(j) that correspond to the candidate areaX_(i). For example, a weight {tilde over (g)}_(i) may be defined to beproportional to exp{−V_(i)}.

Further, the image processing apparatus may determine candidate areasthat are adjacent to each other such that the candidate areas havesimilar weights. The image processing apparatus may calculate aweighting matrix g that minimizes a target function f(g), and determinethe corresponding weight based on the weighting matrix g. The targetfunction f(g) may be defined as shown in Equation 4 below.

f(g)=½g ^(T) Lg+½λ∥g−{tilde over (g)}∥ ²  [Equation 4]

m_(ij) may be an overlapping rate between the i^(th) candidate areaX_(i) and the j^(th) candidate area X_(j). The overlapping rate mayindicate a ratio between a size of sharing area of the i^(th) candidatearea X_(i) and the j^(th) candidate area X_(j) and a size of areaoccupied by at least one of the i^(th) candidate area X_(i) and thej^(th) candidate area X_(j). Q may be an N×N matrix that includesm_(ij). When d_(i) is Σ_(j) m_(ij), and when D is Diag{d₁, . . . ,d_(N)}, a Laplacian matrix L may be calculated by using “D−Q” inEquation 4. “g=[g₁, . . . g_(N)]^(T)” may be a weighting matrix of aweight determined based on a position of each candidate area, and alsobe represented as “g=[{tilde over (g)}₁, . . . , {tilde over(g)}_(N)]^(T)”. Further, in Equation 4, the weighting matrix g may bedefined to have elements, each being greater than or equal to zero (0).

In operation 420, the image processing apparatus may determine apost-feature-regression position of the candidate area based on therespective weight, the respective position offset, and the respectiveposition of each of the candidate areas. The image processing apparatusmay calculate the target position offset T for tracking the target byapplying Equation 5 below.

T=Σ _(i) g _(i)(X _(i) ^(D) +T _(i))  [Equation 5]

In Equation 5, X_(i) ^(D) denotes a position vector that indicates arepresentative position of the i^(th) candidate area X_(i). In thisexample, a post-feature-regression position of the i^(th) candidate areaX_(i) may be calculated by using “X_(i) ^(D)+T_(i)”. The imageprocessing apparatus may calculate the target position offset T acquiredafter a total regression by using a weight g_(i) that corresponds toeach of the candidate areas.

In operation 430, the image processing apparatus may determine a finalselected area that includes the target by using the target positionoffset T. The image processing apparatus may determine a post-regressionposition of each of the candidate areas based on the target positionoffset T. The image processing apparatus may determine a final selectedarea in a current frame by applying the calculated target positionoffset T to a position of a prestored basis image.

The image processing apparatus may store a second frame in which a finalselected area is determined, in a memory. When the number of framesstored in the memory exceeds a threshold, the image processing apparatusmay update the basis image by using the stored frame. The imageprocessing apparatus may select a basis image to be updated, based on anassessment result value of a final selected area that corresponds toeach of the stored frames.

FIG. 5 is a flowchart illustrating a process of training an imageprocessing apparatus on a feature regression matrix, according to anexample embodiment. Referring to FIG. 5, the image processing apparatusmay extract a plurality of candidate areas by using a basis image of asample frame in operation 510. The image processing apparatus maycalculate a position offset matrix between the basis image and theplurality of candidate areas in operation 520. The image processingapparatus may determine a feature regression matrix associated with aninput image based on the calculated position offset matrix in operation530.

In operation 510, the image processing apparatus may extract a pluralityof candidate areas by using a basis image of a sample frame. The imageprocessing apparatus may sample N candidate areas of an adjacent areabased on a position of a basis image. The image processing apparatus mayextract the plurality of candidate areas such that a positiondistribution of a candidate area is based on an equal distribution or aGaussian distribution.

The image processing apparatus may extract candidate areas, each havinga set size. For example, a single candidate area may be defined as a32-by-32 pixel block space. In addition, a size of a candidate area maybe the same as a size of a prestored basis image.

In operation 520, the image processing apparatus may calculate aposition offset matrix by using positions of the basis image and theplurality of candidate areas. The image processing apparatus maycalculate a difference between a representative position of each of theplurality of candidate areas and a position of the basis image as aposition offset. The representative position of the candidate area maybe, for example, a position of a center point in the candidate area. Theimage processing apparatus may compare a representative position (x₁,y₁) of a first candidate area to the position of the basis image inorder to calculate a first position offset (P₁ ^(x), P₂ ^(x)) Likewise,the image processing apparatus may calculate a plurality of positionoffsets of an X coordinate and a plurality of position offsets of a Ycoordinate that correspond to each of the plurality of candidate areasas “P₁ ^(x), . . . , P_(N) ^(x)” and “P₁ ^(y), . . . , P_(N) ^(y)”,respectively. Further, the image processing apparatus may calculate aposition offset matrix “C=[C₁, C₂]” by using the plurality of calculatedposition offsets. For example, C₁, and C₂ may be defined as C₁=[P₁ ^(x),. . . . P_(N) ^(x)]^(T), C₂=[P₁ ^(y), . . . , P_(N) ^(y)]^(T),respectively.

In operation 530, the image processing apparatus may determine a featureregression matrix associated with an input image based on the calculatedposition offset matrix. The image processing apparatus may apply theposition offset matrix to respective feature points included in each ofthe candidate areas and feature points in the basis image in order todetermine the feature regression matrix that corresponds to a sampleframe and store the determined feature regression matrix.

The image processing apparatus may calculate a feature regression matrixH(h₁, h₂) by minimizing a target function f(H) by applying Equation 6below.

f(H)=Σ_(i)(h ₁ ^(T) q _(i) −P _(i) ^(x))² +γ∥h ₁∥²+Σ_(i)(h ₂ ^(T) q _(i)−P _(i) ^(y))² +γ∥h ₂∥²  [Equation 6]

In Equation 6, a feature point that corresponds to the i^(th) candidatearea X_(i) may be q_(i), feature regression vectors for an X coordinateand a Y coordinate may be respectively h₁ and h₂, and γ is a constant.The image processing apparatus may calculate the feature regressionmatrix H based on a logistic regression. The feature regression matrix Hcalculated by the image processing apparatus may be represented as shownin Equation 7 below.

H=(XX ^(T) +γI)⁻¹ XC  [Equation 7]

In Equation 7, X denotes a matrix associated with a representativeposition of a candidate area and I denotes a unit matrix that has anelement of one (1) on a diagonal line and remaining elements of zero (0)(also referred to herein as an “identity matrix”). The image processingapparatus may store a learned feature regression matrix in a memory.

The image processing apparatus may train M feature regression matricesby using M sample frames that vary from one another and store the Mfeature regression matrices. In this example, a single sample frame maycorrespond to a single feature regression matrix and M may be a positiveinteger greater than or equal to two (2). The image processing apparatusmay increase a target tracking accuracy by effectively removing anoutlier via the use of a plurality of feature regression matrices. Thelearned feature regression matrix with training may be used to track atarget of a predetermined frame included in an input image. Since thedescriptions of FIGS. 1, 2, 3A, 3B, and 4 are applicable here, repeateddescription about the target tracking process will be omitted forbrevity.

FIG. 6 is a flowchart illustrating a process of determining a finalselected area that includes a target by using an image processingapparatus, according to an example embodiment. Referring to FIG. 6, amethod of determining a final selected area may include operation 610,in which a respective similarity between a positive sample associatedwith a target feature and each of a plurality of candidate areas iscalculated, and operation 620, in which a final selected area thatincludes a target is determined in a frame based on each calculatedsimilarity.

In operation 610, the image processing apparatus may calculate arespective similarity between the positive sample and each of theplurality of candidate areas by using a stored clustering model. Theimage processing apparatus may determine an initial selected areaassociated with the target in an input frame and extract a plurality ofcandidate areas based on the determined initial selected area. Since thedescriptions of FIGS. 3A and 3B are applicable here, repeateddescription about a process in which the image processing apparatusextracts a plurality of candidate areas will be omitted for brevity.

The clustering model may include a positive sample matrix that isdetermined by extracting a feature of a target area in a basis image asa positive sample. Further, the clustering model may include a negativesample matrix that is determined by extracting a feature of an ambientarea in a predetermined range based on the target area as a negativesample. For example, at least one of a gray image and a histogram oforiented gradients may be used as a feature of an image.

In operation 610, the image processing apparatus may calculate therespective similarity between the positive sample and each of theplurality of candidate areas by using an SSC model trained based on aplurality of basic sub-images. The SSC model may be implemented as, forexample, a hybrid sparse subspace clustering model (HSSC) model thatincludes a positive sample matrix and a negative sample matrix.

In operation 610, the image processing apparatus may calculaterespective similarities between feature points included in eachcandidate area and the positive sample matrix included in the HSSCmodel. Further, the image processing apparatus may calculate thesimilarities by comparing a feature point of a subarea included in thecandidate area to the positive sample matrix. The image processingapparatus may add up similarities associated with a plurality ofsubareas included in each of the candidate areas.

The image processing apparatus may determine the similarity thatcorresponds to the candidate area based on a sum of the similaritiesassociated with the plurality of subareas included in the candidatearea. The image processing apparatus may calculate an average value ofthe similarities associated with the plurality of subareas as thesimilarity of the candidate area.

In operation 620, the image processing apparatus may determine the finalselected area that includes the target in the frame based on therespective similarity that corresponds to each of the candidate areas.The image processing apparatus may determine a candidate area that has amaximum similarity with respect to a positive sample among the pluralityof candidate areas, as a final selected area associated with the target.In addition, the image processing apparatus may store information thatrelates the determined final selected area as a target tracking resultof a current frame. The information that relates to the final selectedarea may include at least one of, for example, a size of an area, aposition of the area, data on an image in the area, and data on afeature in the image.

The image processing apparatus may compare the final selected area ofthe frame to previous frames stored in the memory. The image processingapparatus may compare an average similarity value of the previous frameswith respect to the positive sample to the similarity of the finalselected area with the positive sample. As a comparison result, when thesimilarity of the final selected area with respect to the positivesample is greater than the average similarity value of the previousframes with respect to the positive sample, the image processingapparatus may newly store a frame in which the final selected area isdetermined. Further, the image processing apparatus may newly store abasis image of a target in a frame that has a maximum similarity withrespect to the positive sample among the prestored frames.

FIGS. 7A and 7B are diagrams illustrating a process of extracting apositive sample and a negative sample based on a sample frame by usingan image processing apparatus, according to an example embodiment.Referring to FIG. 7A, an image processing apparatus 700 may receive asample frame and output either a positive sample or a negative samplethat corresponds to the sample frame. The image processing apparatus 700may be previously trained to extract the positive sample or the negativesample from a determined sample frame. The image processing apparatus700 may store a positive sample matrix 721 and a negative sample matrix722 generated by using a sample frame in a memory 710 as a clusteringmodel.

Referring to FIG. 7B, the image processing apparatus 700 may extractfeature points 731, 732, 733, 734, 735, 736, 737, and 738 in an inputsample frame as a positive sample. The image processing apparatus 700may extract feature points such as a face contour point, an eye point, amouth point, and the like of a target included in a basic image of asample frame as the positive sample. Further, the image processingapparatus 700 may sample a plurality of pixel points in an area 740within a predetermined distance d based on the feature points extractedas the positive sample, and may extract the plurality of pixel points asa negative sample. The predetermined distance d may be a parameterdesignated to distinguish between the positive sample and the negativesample.

FIG. 8 is a flowchart illustrating a process of training the imageprocessing apparatus of FIG. 7A on an SSC model. Referring to FIG. 8,the image processing apparatus 700 may calculate a coefficient matrixthat defines a subarea of a positive sample by using a positive samplematrix in operation 810, and may perform spectral clustering by usingthe calculated coefficient matrix in operation 820.

In operation 810, the image processing apparatus 700 may optimize aproduction coefficient matrix by using a predetermined positive samplematrix. When N positive samples are provided, a positive sample may beI_(i) ⁺, for example, i=1, . . . , N, and a positive sample matrix A maybe defined as, for example, A=[I₁ ⁺, . . . , I_(N) ⁺]. In addition, whenM negative samples are provided, a negative sample may be defined asI_(j) ⁻, for example, j=1, . . . , M. Since the descriptions of FIGS. 7Aand 7B are also applicable here, repeated descriptions about the processin which the image processing apparatus 700 extracts a positive samplefrom at least one sample frame and generating a positive sample matrixwill be omitted for brevity.

In operation 810, the image processing apparatus 700 may optimize theproduction coefficient matrix based on a least squares regression (LSR)model. The image processing apparatus 700 may calculate an optimalproduction coefficient matrix W* by minimizing a target function f(W) inaccordance with Equation 8 below.

f(W)=∥A−AW∥ _(F) ² +λ∥W∥ _(F) ²  [Equation 8]

In Equation 8, W denotes a production coefficient matrix, λ is aconstant, and ∥_(F) denotes a matrix F. The image processing apparatus700 may calculate the optimal production coefficient matrix W* thatminimizes the target function defined f(W) by using Equation 8 accordingto Equation 9.

W*=[A ^(T) A+λI] ⁻¹ A ^(T) A  [Equation 9]

In Equation 9, A^(T) denotes a transposed matrix of a positive samplematrix and [A^(T)A+λI]⁻¹ may be an inverse matrix of [A^(T)A+λI]. Theimage processing apparatus 700 may calculate a mixing coefficient matrixB by applying the calculated optimal production coefficient matrix W* toEquation 10.

B=(|W*|+|(W*)^(T)|)  [Equation 10]

In operation 820, the image processing apparatus 700 may perform thespectral clustering by using the calculated coefficient matrix. Theimage processing apparatus 700 may perform spectral clustering on thegenerated mixing coefficient matrix B and acquire a plurality ofpositive sample groups.

The image processing apparatus 700 may repetitively perform the spectralclustering on the mixing coefficient matrix B by the preset number oftimes and acquire the plurality of positive sample groups. The imageprocessing apparatus 700 may perform the spectral clustering until Npositive samples are clustered into K positive sample groups, K being aninteger less than or equal to N. The spectral clustering process iswell-known to persons having ordinary skill in the art and thus, relateddescription will be omitted.

The image processing apparatus 700 may count the number of times thatthe spectral clustering is performed on the mixing coefficient matrix Bas an index and determine whether to repeat the spectral clusteringbased on the counted index. When the spectral clustering is repeated Ktimes with respect to the mixing coefficient matrix B, the imageprocessing apparatus 700 may store a positive sample group generated ina K^(th) spectral clustering operation in the HSSC model and terminatethe repeating of the spectral clustering.

The image processing apparatus 700 may calculate an identifiabilitycoefficient matrix that determines whether to repeat the spectralclustering using the positive sample group and the negative sample. Theimage processing apparatus 700 may extract a positive sample and anegative sample that correspond to a predetermined k^(th) positivesample group as a single sample group. The image processing apparatus700 may acquire an identification direction p_(k) that corresponds tothe positive sample in the sample group based on a predetermined graphembedding model. Hereinafter, a graph embedding model may indicate amethod of mapping a graph to another graph.

The image processing apparatus 700 may determine a weight of samplesbased on a Euclidean distance of a positive sample and a negative sampleincluded in a sample group. For example, a k^(th) group may include apositive sample I_(i) ⁺ and a negative sample I_(j) ⁻. In this example,the image processing apparatus 700 may calculate a Euclidean distancebetween two samples as d_(ij), and calculate a weight exp{−d_(ij)} basedon the Euclidean distance d_(ij). When the two samples are positivesamples or negative samples, the image processing apparatus 700 maycalculate a weight between the two samples as zero (0). Further, theimage processing apparatus 700 may calculate a Laplacian matrix used forthe graph embedding based on the calculated weight and acquire theidentification direction p_(k) based on the Laplacian matrix.

The image processing apparatus 700 may determine a similarity betweenthe positive sample and an average value of the positive sample groupbased on the identification direction p_(k) of each sample group. Theimage processing apparatus 700 may calculate a similarity l_(i) ^(k)between a positive sample I_(i) ⁺ and an average value Ī_(k) of apositive sample group by applying Equation 11.

l _(i) ^(k)=exp{−|p _(k) ^(T)(I _(i) ⁺ −Ī _(k))|}  [Equation 11]

Further, the image processing apparatus 700 may calculate anidentifiability coefficient matrix based on the similarity l_(i) ^(k)calculated by using Equation 11. The image processing apparatus 700 maycalculate a similarity coefficient {tilde over (w)}_(ij) based on anidentifiability between the positive sample I_(i) ⁺ and a positivesample I_(j) ⁺ by applying Equation 12.

{tilde over (w)} _(ij)∝max{l _(i) ¹ l _(j) ¹ , . . . ,l _(i) ^(K) l _(j)^(K)}  [Equation 12]

In Equation 12 I_(i) ¹ denotes a similarity between the positive sampleI_(i) ⁺ and an average value Ī₁ of a first positive sample group andI_(j) ¹ denotes a similarity between the positive sample I_(j) ⁺ and anaverage value Ī₁ of the first positive sample group. In addition, theimage processing apparatus 700 may acquire an identifiabilitycoefficient matrix {tilde over (W)} by using the similarity coefficient{tilde over (w)}_(ij) calculated using Equation 12 as an element.

The image processing apparatus 700 may verify the number of positivesamples included in each positive sample group. When the verified numberof positive samples is less than a threshold, the image processingapparatus 700 may determine that a positive sample group thatcorresponds to a positive sample is in a vacancy state.

The image processing apparatus 700 may add a positive sample to thefirst sample group that is in the vacancy state. When a similaritybetween a positive sample in a second positive sample group and thefirst positive sample group is greater than or equal to a threshold, theimage processing apparatus 700 may add the positive sample to thepositive sample group. The image processing apparatus 700 mayrepetitively add a positive sample such that the number of positivesamples in the first sample group is greater than or equal to thethreshold.

The image processing apparatus 700 may acquire the mixing coefficientmatrix B by using the optimal production coefficient matrix W* and theidentifiability coefficient matrix {tilde over (W)} in accordance withEquation 13.

B=α(|W*|+|(W*)^(T)|)+(1−α){tilde over (W)}  [Equation 13]

In Equation 13, α denotes a constant. The image processing apparatus 700may perform the spectral clustering on the mixing coefficient matrix Band generate a respective positive sample group that corresponds to eachspectral clustering operation. The image processing apparatus 700 mayperform the spectral clustering on the mixing coefficient matrix B untilthe number of times that the spectral clustering is repeated reaches apredefined threshold.

The image processing apparatus 700 may perform a principal componentanalysis (PCA) for each of the generated positive sample groups andacquire a subarea of the corresponding positive sample group. Thesubarea of the positive sample group may be included in the HSSC model.Further, the subarea may include an average value of positive samplesincluded in the subarea.

The image processing apparatus 700 may generate a clustering model byusing a positive sample associated with a target. In addition, the imageprocessing apparatus 700 may generate an HSSC model that corresponds toa subarea in a positive sample. Using the HSSC model, the imageprocessing apparatus 700 may achieve a robustness to image noise andincrease accuracy on tracking a moving target. The HSSC model may bedetermined by using a plurality of sample frames based on an Euclideandistance between the positive sample associated with a feature of thetarget and a negative sample associated with a feature of an area thatis adjacent to the target.

The image processing apparatus 700 may update the HSSC model. The imageprocessing apparatus 700 may determine whether the number of framesstored in a memory is greater than a preset threshold. In this aspect,each of the frames may be, for example, a frame in which the finalselected area is determined. When the number of the stored frames isgreater than the threshold, the image processing apparatus 700 mayextract a new sample frame from an input image and update the HSSC modelby using the extracted sample frame. The image processing apparatus 700may extract a subarea of a positive sample group based on the extractedsample frame.

Further, the image processing apparatus 700 may update the basis imagebased on the extracted sample frame. The image processing apparatus 700may perform target tracking on a subsequently input frame by using theupdated basis image and the HSSC model. Since the description of FIG. 6is also applicable here, repeated description about the process in whichthe image processing apparatus 700 tracks a target will be omitted forbrevity.

FIG. 9 is a block diagram illustrating an image processing apparatus,according to an example embodiment. Referring to FIG. 9, an imageprocessing apparatus 900 may include an extractor 910, a firstcalculator 920, a second calculator 930, and a determiner 940. Theextractor 910 may extract a plurality of candidate areas from an inputimage by using a basis image. The basis image may be an image of an areathat includes a target in a frame stored in the image processingapparatus 900. The basis image may be included in a first frame of theinput image that includes the target. Further, the basis image may beincluded in a predetermined frame of the input image as a final selectedarea in which the target is tracked by the image processing apparatus900.

The first calculator 920 may track the target based on respectivefeature points included in each of the plurality of candidate areas. Thefirst calculator 920 may calculate a plurality of position offsets byapplying a feature regression matrix to each of the plurality ofcandidate areas. In addition, the first calculator 920 may calculate atarget position offset by applying a respective weight to eachcorresponding one of the plurality of calculated position offsets.

The second calculator 930 may calculate a respective similarity betweena positive sample associated with a target feature and each of theplurality of candidate areas. The second calculator 930 may calculateeach respective similarity by using an HSSC model. The HSSC model may betrained by using a positive sample associated with the target and anegative sample associated with an area that is adjacent to the target.The second calculator 930 may calculate a similarity between a subareaincluded in a candidate area and a positive sample by applying Equation14.

L _(r) ^(k)=exp{−∥(I−Ī)−U _(k) U _(k) ^(T)(I−Ī)∥²}  [Equation 14]

In Equation 14, U_(k) denotes a k^(th) partial space included in theHSSC model, I denotes a feature of a positive sample included in eachpartial space, Ī denotes a feature average value of positive samplesincluded in each partial space, and r denotes an index of a subarea in acandidate area. I may represent, for example, a brightness value of thepositive sample.

Further, when a partial space among partial spaces included in the HSSCmodel has a maximal similarity with an r^(th) subarea, the secondcalculator 930 may determine the maximal similarity as a similarity thatcorresponds to the r^(th) subarea. The second calculator 930 maycalculate the similarity that corresponds to the r^(th) subarea byapplying Equation 15.

L _(r)=max_(k) L _(r) ^(k)  [Equation 15]

The second calculator 930 may add up respective similarities thatcorrespond to all subareas included in an i^(th) candidate area X_(i) soas to calculate a similarity that corresponds to the i^(th) candidatearea X_(i) by applying Equation 16.

L(X _(i))=Σ_(r) L _(r)  [Equation 16]

The determiner 940 may apply a first weight to the target positionoffset and apply a second weight to a respective similarity thatcorresponds to each of the plurality of candidate areas in order todetermine a final selected area that includes the target. The determiner940 may calculate assessment information that corresponds to the finalselected area by applying Equation 17.

{tilde over (X)}=βX ^(S)+(1−β)X ^(R)  [Equation 17]

In Equation 17, X^(S) denotes a maximal similarity of a candidate area,X^(R) denotes a target position offset, β denotes a weight coefficient,and {tilde over (X)} denotes assessment information of a final selectedarea that includes a target, β being a real number greater than zero (0)and less than one (1).

The above-described example embodiments may be recorded innon-transitory computer-readable media that include program instructionsto implement various operations which may be performed by a computer.The media may also include, alone or in combination with the programinstructions, data files, data structures, and the like. The programinstructions recorded on the media may be those specially designed andconstructed for the purposes of the example embodiments, or they may beof the well-known kind and available to persons having ordinary skill inthe computer software arts. Examples of non-transitory computer-readablemedia include magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as compact disc-read-only memory (CDROM) discs and digital versatile discs (DVDs); magneto-optical mediasuch as optical discs; and hardware devices that are speciallyconfigured to store and perform program instructions, such as read-onlymemory (ROM), random access memory (RAM), flash memory, and the like.The media may include transfer media such as optical lines, metal lines,or waveguides including a carrier wave for transmitting a signaldesignating the program command and the data construction. Examples ofprogram instructions include both machine code, such as code produced bya compiler, and files containing higher level code that may be executedby the computer using an interpreter. The described hardware devices maybe configured to act as one or more software modules in order to performthe operations of the above-described example embodiments, or viceversa.

The foregoing example embodiments are examples and are not to beconstrued as limiting. The present disclosure can be readily applied toother types of apparatuses. Also, the description of the exampleembodiments is intended to be illustrative, and not to limit the scopeof the claims, and many alternatives, modifications, and variations willbe apparent to persons having ordinary skill in the art.

What is claimed is:
 1. An image processing apparatus comprising: aprocessor configured to implement: a calculator configured to calculatea respective first position offset for each of a plurality of candidateareas in a second frame based on a position of a basis image in a firstframe; and a determiner configured to determine a final selected areathat includes a target in the second frame based on a respective weightallocated to each of the plurality of candidate areas and the calculatedrespective first position offset.
 2. The image processing apparatus ofclaim 1, wherein the determiner is further configured to determine eachrespective weight based on a respective position of each correspondingone of the plurality of candidate areas in the second frame.
 3. Theimage processing apparatus of claim 1, wherein the calculator is furtherconfigured to calculate a plurality of second position offsets byapplying a feature regression matrix to each of the plurality ofcandidate areas, and to calculate a target position offset for trackingthe target by applying each respective weight to each corresponding oneof the plurality of second position offsets.
 4. The image processingapparatus of claim 1, wherein the calculator is further configured tocalculate a plurality of second position offsets for a first candidatearea by using a plurality of predetermined feature regression matrices,and to calculate the first position offset that corresponds to the firstcandidate area by using an average value of the plurality of secondposition offsets.
 5. The image processing apparatus of claim 4, whereinthe plurality of predetermined feature regression matrices is determinedbased on a respective third position offset that corresponds to arespective feature point of each of a plurality of sample frames and afeature point in the basis image.
 6. The image processing apparatus ofclaim 1, further comprising an extractor configured to determine aninitial selected area associated with the target in the second framebased on the basis image in the first frame and to extract the pluralityof candidate areas based on the determined initial selected area.
 7. Theimage processing apparatus of claim 6, wherein the extractor is furtherconfigured to calculate an overall position offset between the firstframe and the second frame and to determine the initial selected areabased on the calculated overall position offset and information thatrelates to a position at which the target is present in the basis image.8. The image processing apparatus of claim 7, wherein the extractor isfurther configured to extract, from the second frame, respectiveprojection points that correspond to feature points in the basis imageand to determine the overall position offset by using a respectivetexture value of each of a plurality of first points in a predeterminedrange and the extracted projection points.
 9. The image processingapparatus of claim 8, wherein the extractor is further configured toextract the plurality of first points in the predetermined range basedon the extracted projection points, to determine matching points thatcorrespond to feature points based on a respective similarity between atexture value of each corresponding one of the extracted plurality offirst points and a texture value of each of the feature points, and todetermine the overall position offset by comparing a respective positionof each of the feature points with a respective position of each of thematching points.
 10. The image processing apparatus of claim 1, furthercomprising a storage configured to store the second frame in which thefinal selected area is determined and to update, when a number of storedframes is greater than or equal to a threshold, the basis image based ona target tracking result value of the stored frames.
 11. An imageprocessing method comprising: calculating a respective similaritybetween a positive sample associated with a target and each of aplurality of candidate areas; and determining, from among the pluralityof candidate areas, a final selected area that includes the target in aframe based on each calculated respective similarity.
 12. The imageprocessing method of claim 11, wherein the calculating the respectivesimilarity includes comparing a feature of the positive sample includedin a sparse subspace clustering (SSC) model to a feature of a respectivesubarea in each of the plurality of candidate areas.
 13. The imageprocessing method of claim 12, wherein the calculating the respectivesimilarity includes calculating a similarity that corresponds to a firstcandidate area based on a sum of similarities between a plurality ofsubareas included in the first candidate area.
 14. The image processingmethod of claim 12, wherein the SSC model is determined by using aplurality of sample frames based on a Euclidean distance between thepositive sample associated with the target and a negative sampleassociated with a feature of an area that is adjacent to the finalselected area that includes the target.
 15. The image processing methodof claim 11, further comprising: comparing a similarity between thepositive sample and the final selected area of the frame to an averagevalue of similarities between the positive sample and previous frames;and storing the frame based on a result of the comparing.
 16. The imageprocessing method of claim 15, further comprising: comparing a number ofstored frames to a threshold and updating an SSC model by using thestored frames as sample frames based on a result of the comparing thenumber of stored frames to the threshold.
 17. An image processingapparatus comprising: a processor, wherein the processor is configuredto implement: an extractor configured to extract a plurality ofcandidate areas from an input frame by using a basis image; a firstcalculator configured to calculate a target position offset for trackingrespective feature points included in each of the plurality of candidateareas; a second calculator configured to calculate a respectivesimilarity between each of the plurality of candidate areas and apositive sample associated with a target; and a determiner configured todetermine a final selected area that includes the target by applying afirst weight to the target position offset and applying a respectivesecond weight to each respective similarity between the correspondingone of the plurality of candidate areas and the positive sample.
 18. Theimage processing apparatus of claim 17, wherein the first calculator isfurther configured to calculate a plurality of first position offsets byapplying a feature regression matrix to each corresponding one of theplurality of candidate areas, and to calculate the target positionoffset by applying a weight to the plurality of first position offsets.19. The image processing apparatus of claim 17, wherein the secondcalculator is further configured to calculate each respective similaritybased on a hybrid sparse subspace clustering (HSSC) model that isdetermined by using the positive sample associated with the target and anegative sample associated with an area that is adjacent to the finalselected area that includes the target.